code: def test_default_configuration_classify(self):
      for i in range(5):
          X_train, Y_train, X_test, Y_test = get_dataset(
              dataset="digits",
              make_sparse=False,
              train_size_maximum=1000,
          )
          configuration_space = (
              KernelPCA.get_hyperparameter_search_space()
          )
          default = configuration_space.get_default_configuration()
          preprocessor = KernelPCA(
              random_state=1,
              **{
                  hp_name: default[hp_name]
                  for hp_name in default
                  if default[hp_name] is not None
              },
          )
          preprocessor.fit(X_train, Y_train)
          X_train_trans = preprocessor.transform(X_train)
          X_test_trans = preprocessor.transform(X_test)
  
          # fit a classifier on top
          classifier = RidgeClassifier(random_state=1)
          predictor = classifier.fit(X_train_trans, Y_train)
          predictions = predictor.predict(X_test_trans)
          accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
          self.assertAlmostEqual(accuracy, 0.6775407779171895)
  
file_path: auto-sklearn/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py
imports: import sklearn.metrics
  from sklearn.linear_model import RidgeClassifier
  from autosklearn.pipeline.components.feature_preprocessing.kernel_pca import KernelPCA
  from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing, get_dataset
  import unittest
label_desc: Use image node_id 3 for calling the KernelPCAComponentTest obj's underlying member method code with example usage: obj.test_default_configuration_classify() without return types